## Reading layer `camp_fire_buffer' from data source
## `/Users/josiezenger/Local/Dissertation/NTL_Wildfire_Recovery/camp_fire_buffer/camp_fire_buffer.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 1 feature and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -13566250 ymin: 4797781 xmax: -13498910 ymax: 4861104
## Projected CRS: WGS 84 / Pseudo-Mercator
## Reading layer `camp_fire' from data source
## `/Users/josiezenger/Local/Dissertation/NTL_Wildfire_Recovery/camp_sf/camp_fire.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 1 feature and 21 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -13556250 ymin: 4807780 xmax: -13508910 ymax: 4851104
## Projected CRS: WGS 84 / Pseudo-Mercator
# Load raster file paths
camp_outputs <- list.files("~/Local/Dissertation/CampFire_TIFFs", full.names = TRUE)
new_outputs <- list.files("~/Local/Dissertation/NTL_Wildfire_Recovery/CampFire_2_TIFFs", full.names = TRUE)
cbuff_outputs <- list.files("~/Local/Dissertation/CBuff_tiffs", full.names = TRUE)
cb.new_outputs <- list.files("~/Local/Dissertation/NTL_Wildfire_Recovery/CBuff_2_tiffs", full.names = TRUE)
# Load CSVs
camp_csv <- read.csv("~/Local/Dissertation/NTL_Wildfire_Recovery/Camp_DailyMeanLuminosity.csv")
cbuff_csv <- read.csv("~/Local/Dissertation/NTL_Wildfire_Recovery/CBuff_DailyMeanLuminosity.csv")
# Load and aggregate rasters
load_rasters <- function(file_paths, prefix_to_remove) {
rasters <- lapply(file_paths, function(path) {
r <- rast(path)
aggregate(r, fact = 3, fun = mean, na.rm = TRUE) # aggregate pixels
})
names(rasters) <- tools::file_path_sans_ext(basename(file_paths))
names(rasters) <- gsub(prefix_to_remove, "", names(rasters))
rasters
}
# Combine the additional years of data that I extracted (this is not a necessary step in most cases)
camp_data <- load_rasters(c(camp_outputs, new_outputs), "Camp_")
cbuff_data <- load_rasters(c(cbuff_outputs, cb.new_outputs), "CBuff_")
# Extract and align dates
get_week_df <- function(raster_list) {
tibble(
name = names(raster_list),
date = as.Date(names(raster_list)),
week = floor_date(as.Date(names(raster_list)), unit = "week", week_start = 1)
)
}
camp_weeks_df <- get_week_df(camp_data)
cbuff_weeks_df <- get_week_df(cbuff_data)
# Composite rasters by week
composite_by_week <- function(week_df, raster_list) {
week_df %>%
group_by(week) %>%
group_split() %>%
setNames(map_chr(., ~ as.character(unique(.$week)))) %>%
map(~ {
rasters_to_stack <- lapply(.$name, function(n) raster_list[[n]])
composite <- app(rast(rasters_to_stack), fun = mean, na.rm = TRUE)
composite
})
}
camp_weekly_rasters <- composite_by_week(camp_weeks_df, camp_data)
cbuff_weekly_rasters <- composite_by_week(cbuff_weeks_df, cbuff_data)
# Convert to list with metadata
combine_data <- function(raster_list, unit_label) {
lapply(names(raster_list), function(name) {
list(
raster = raster_list[[name]],
date = as.Date(name),
unit_type = unit_label
)
}) %>% setNames(names(raster_list))
}
c.combined_data <- combine_data(camp_weekly_rasters, "Treatment")
cb.combined_data <- combine_data(cbuff_weekly_rasters, "Control")
# Combine both treatment and control rasters
total_combined_data <- c(c.combined_data, cb.combined_data)
## Length Class Mode
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## 2018-05-01 2 -none- list
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# In this section of code, I will visualize the weekly composites of the Camp Fire data and the buffer data. This was mostly an exploration of how different weeks looked and what would be appropriate to put into the final visualization.
example_week_4 <- names(camp_weekly_rasters)[2]
plot(camp_weekly_rasters[[example_week_4]],
main = paste("Weekly Composite:", example_week_4))
example_week_3 <- names(camp_weekly_rasters)[28]
plot(camp_weekly_rasters[[example_week_3]],
main = paste("Weekly Composite:", example_week_3))
example_week <- names(camp_weekly_rasters)[31]
plot(camp_weekly_rasters[[example_week]],
main = paste("Weekly Composite:", example_week))
rasters_to_plot <- c(
camp_weekly_rasters[[example_week_4]],
camp_weekly_rasters[[example_week_3]],
camp_weekly_rasters[[example_week]]
)
# Plot in a 3 x 1 layout
plot(rasters_to_plot,
main = c(paste("Weekly Composite:", example_week_4),
paste("Weekly Composite:", example_week_3),
paste("Weekly Composite:", example_week)),
nc = 1)
# Here I create buffer composites, just like above.
example_week_5 <- names(cbuff_weekly_rasters)[2]
plot(cbuff_weekly_rasters[[example_week_5]],
main = paste("Weekly Composite:", example_week_5))
example_week_2 <- names(cbuff_weekly_rasters)[28]
plot(cbuff_weekly_rasters[[example_week_2]],
main = paste("Weekly Composite:", example_week_2))
example_week_6 <- names(cbuff_weekly_rasters)[31]
plot(cbuff_weekly_rasters[[example_week_6]],
main = paste("Weekly Composite:", example_week_6))
rasters_to_plot_2 <- c(
cbuff_weekly_rasters[[example_week_5]],
cbuff_weekly_rasters[[example_week_2]],
cbuff_weekly_rasters[[example_week_6]]
)
# Plot in a 3 x 1 layout
plot(rasters_to_plot_2,
main = c(paste("Weekly Composite:", example_week_4),
paste("Weekly Composite:", example_week_3),
paste("Weekly Composite:", example_week_6)),
nc = 1)
